Quantitative Temporal Constraints Research Articles

A norm enforcement mechanism for a time-constrained conditional normative framework

This paper presents the formalization for a system that monitors and enforces regulative time-constrained conditional norms through sanctioning, for agent societies. The representation here has the advantage of allowing for qualitative and quantitative interval-based temporal constraints between a norm’s condition and its effect. The system possesses mechanisms for monitoring both norm compliance and violation of reified norms. Each norm has an identity, which enables the identification of norms violated or complied with. The formalism seamlessly treats violation- or conformance-handling norms (such as violation notification, sanctioning and rewarding norms) as regular norms. The implementation formalizes norms as logic program clauses, the head of which specifies what normative position (i.e. the norm’s effect) an agent must observe within some time constraint of some arising situation; and the body of which describes a situation defined as the norm’s pre-condition and the role that the implicated agent plays within it. For the purpose of imposing sanctions, the only violations deemed liable are those violations that the agent fails to mitigate by compliance with all relevant contrary-to-duty norms. An agent may ultimately be sanctioned for a liable norm while norm compliance is rewarded in the system. Sanctioning itself takes place in two norm-guided phases: the obligation of an enforcer agent to notify an erring agent of reparative actions it is obliged to take and the time constraint within which it should be taken, and a norm for monitoring the agent’s compliance with the notification. The violation of the latter norm leads to the meting out of sanctions.

Leveraging semantic labels for multi-level abstraction in medical process mining and trace comparison

Many medical information systems record data about the executed process instances in the form of an event log. In this paper, we present a framework, able to convert actions in the event log into higher level concepts, at different levels of abstraction, on the basis of domain knowledge. Abstracted traces are then provided as an input to trace comparison and semantic process discovery. Our abstraction mechanism is able to manage non trivial situations, such as interleaved actions or delays between two actions that abstract to the same concept. Trace comparison resorts to a similarity metric able to take into account abstraction phase penalties, and to deal with quantitative and qualitative temporal constraints in abstracted traces. As for process discovery, we rely on classical algorithms embedded in the framework ProM, made semantic by the capability of abstracting the actions on the basis of their conceptual meaning. The approach has been tested in stroke care, where we adopted abstraction and trace comparison to cluster event logs of different stroke units, to highlight (in)correct behavior, abstracting from details. We also provide process discovery results, showing how the abstraction mechanism allows to obtain stroke process models more easily interpretable by neurologists.

Probabilistic Quantitative Temporal Constraints: Representing, Reasoning, and Query Answering

In many applications and domains, temporal constraints between actions, and their probabilities play an important role. We propose the first approach in the literature coping with probabilistic quantitative constraints. To achieve such a challenging goal, we extend the widely used simple temporal problem (STP) framework to consider probabilities. Specifically, we propose i) a formal representation of probabilistic quantitative constraints, ii) an algorithm, based on the operations of intersection and composition, for the propagation of such temporal constraints, and iii) facilities to support query answering on a set of such constraints. As a result, we provide users with the first homogeneous method supporting the treatment (representing, reasoning, and querying) of probabilistic quantitative constraints, as required by many applications and domains.

The Post‐Eocene Evolution of the Doruneh Fault Region (Central Iran): The Intraplate Response to the Reorganization of the Arabia‐Eurasia Collision Zone

AbstractThe Cenozoic deformation history of Central Iran has been dominantly accommodated by the activation of major intracontinental strike‐slip fault zones, developed in the hinterland domain of the Arabia‐Eurasia convergent margin. Few quantitative temporal and kinematic constraints are available from these strike‐slip deformation zones, hampering a full assessment of the style and timing of intraplate deformation in Iran and the understanding of the possible linkage to the tectonic reorganization of the Zagros collisional zone. This study focuses on the region to the north of the active trace of the sinistral Doruneh Fault. By combing structural and low‐temperature apatite fission track (AFT) and (U‐Th)/He (AHe) thermochronology investigations, we provide new kinematic and temporal constraints to the deformation history of Central Iran. Our results document a post‐Eocene polyphase tectonic evolution dominated by dextral strike‐slip tectonics, whose activity is constrained since the early Miocene in response to an early, NW‐SE oriented paleo‐σ1 direction. A major phase of enhanced cooling/exhumation is constrained at the Miocene/Pliocene boundary, caused by a switch of the maximum paleo‐σ1 direction to N‐S. When integrated into the regional scenario, these data are framed into a new tectonic reconstruction for the Miocene‐Quaternary time lapse, where strike‐slip deformation in the intracontinental domain of Central Iran is interpreted as guided by the reorganization of the Zagros collisional zone in the transition from an immature to a mature stage of continental collision.

Managing Temporal Constraints with Preferences: Representation, Reasoning, and Querying

Representing and managing temporal knowledge, in the form of temporal constraints, is a crucial task in many areas, including knowledge representation, planning, and scheduling. The current literature in the area is moving from the treatment of “crisp” temporal constraints to fuzzy or probabilistic constraints, to account for preferences and\or uncertainty. Given a set of temporal constraints, the evaluation of the tightest implied constraints is a fundamental task, which is essential also to provide reliable query-answering facilities . However, while such tasks have been widely addressed for “crisp” temporal constraints, they have not attracted enough attention in the “non-crisp” context yet. We overcome such a limitation, by (i) extending quantitative temporal constraints to cope with preferences , (ii) defining a temporal reasoning algorithm which evaluates the tightest temporal constraints, and (iii) providing suitable query-answering facilities based on it.

Temporal detection and analysis of guideline interactions.

Clinical practice guidelines (CPGs) are assuming a major role in the medical area, to grant the quality of medical assistance, supporting physicians with evidence-based information of interventions in the treatment of single pathologies. The treatment of patients affected by multiple diseases (comorbid patients) is one of the main challenges for the modern healthcare. It requires the development of new methodologies, supporting physicians in the treatment of interactions between CPGs. Several approaches have started to face such a challenging problem. However, they suffer from a substantial limitation: they do not take into account the temporal dimension. Indeed, practically speaking, interactions occur in time. For instance, the effects of two actions taken from different guidelines may potentially conflict, but practical conflicts happen only if the times of execution of such actions are such that their effects overlap in time. We aim at devising a methodology to detect and analyse interactions between CPGs that considers the temporal dimension. In this paper, we first extend our previous ontological model to deal with the fact that actions, goals, effects and interactions occur in time, and to model both qualitative and quantitative temporal constraints between them. Then, we identify different application scenarios, and, for each of them, we propose different types of facilities for user physicians, useful to support the temporal detection of interactions. We provide a modular approach in which different Artificial Intelligence temporal reasoning techniques, based on temporal constraint propagation, are widely exploited to provide users with such facilities. We applied our methodology to two cases of comorbidities, using simplified versions of CPGs. We propose an innovative approach to the detection and analysis of interactions between CPGs considering different sources of temporal information (CPGs, ontological knowledge and execution logs), which is the first one in the literature that takes into account the temporal issues, and accounts for different application scenarios.

Geometry-based propagation of temporal constraints

In recent years, the Internet of Things (IoT) has been introduced to offer promising solutions in many areas. A big challenge faced by the IoT is to integrate heterogeneous information sources and process information effectively. As an important element in information integration, temporal reasoning is highly related to the dynamic, sequential aspect of both the information integration and the decision making process. Focusing on temporal reasoning, this paper introduces a method to represent both qualitative and quantitative temporal constraints in a 2-dimensional (2-D) space. Meanwhile, an efficient constraint-based geometric (CG) algorithm for propagating constraints (including inherent constraints and constraint pairs) on events in a 2-D space is proposed. A geometric recombination and intersection (GRI) method, a part of the CG algorithm, is presented to propagate one constraint pair from a geometric point. The experimental results show that in terms of both constructed and realistic benchmarks, the CG algorithm outperforms the existing Floyd-Warshall's algorithm with the time complexity of O(n3), especially for benchmarks with a large number of events.

Automatic Generation of System Level Assertions from Transaction Level Models

We automatically generate assertions from Transaction Level Model (TLM) simulation traces. The generated assertions express design specifications in the form of linear temporal logic with quantitative temporal constraints [4]. We first generate the assertions without regard to the quantitative time constraints. They are mined in the form of frequent patterns in the simulation traces. We mine simulation traces using episode mining to identify frequent episodes comprising function calls and events. We then annotate the episodes with real time parameters to express quantitative time constraints among the function calls or events in the episode. When mining such TLM assertions, we employ symbolic execution to generalize the parameters and return values of function calls in the traces to help the mining engine generate high quality assertions. We have constructed a realistic AXI-based interconnection network platform that we demonstrate experimental results on. We show that our technique efficiently generates high quality performance and functional assertions on the AXI-based platform as well as a transaction level AMBA-based DMA controller. We demonstrate that episode mining is more scalable and able to generate a more compact set of high quality TLM assertions than previous efforts using sequential pattern mining. The number of generated assertions using episode mining can be reduced by up to 228 times, and the time interval between two events/function calls in each assertion is smaller than 50 time units.

Specifying safety-critical systems with a decidable duration logic

Punctual timing constraints are important in formal modelling of safety-critical real-time systems. But they are very expensive to express in dense time. In most cases, punctuality and dense-time lead to undecidability. Efforts have been successful to obtain decidability; but the results are either non-primitive recursive or nonelementary. In this paper we propose a duration logic which can express quantitative temporal constraints and punctuality timing constraints over continuous intervals and has a reasonable complexity. Our logic allows most specifications that are interesting in practice, and retains punctuality. It can capture the semantics of both events and states, and incorporates the notions duration and accumulation. We call this logic ESDL (the acronym stands for Event- and State-based Duration Logic). We show that the satisfiability problem is decidable, and the complexity of the satisfiability problem is NEXPTIME. ESDL is one of a few decidable interval temporal logics with metric operators. Through some case studies, we also show that ESDL can specify many safety-critical real-time system properties which were previously specified by undecidable interval logics or their decidable reductions based on some abstractions.

Specifying and solving symbolic and numeric temporal constraints

Representing and solving combinatorial problems, especially those including temporal constraints, using a constraint programming language remains a challenging task. In this paper, we present a tool to assist users in specifying and solving problems under qualitative and quantitative temporal constraints. The tool is based on the TemPro framework that has the ability to manage both numeric and symbolic temporal constraints within a unique model. Our tool provides a generic template that can be specialized to describe a wide variety of temporal constraint applications. Given a problem under temporal constraints, the proposed tool with its friendly graphical user interface first assists the user in the different steps of the problem specification. The graph representation of the temporal constraint problem and its consistent scenarios are then automatically generated and visualized during the solving phase. The user has also the ability to add or remove some constraints and see the effects of these changes on the consistency of the problem.

Efficient variable elimination for semi-structured simple temporal networks with continuous domains

AbstractThe Simple Temporal Network (STN) is a widely used framework for reasoning about quantitative temporal constraints over variables with continuous or discrete domains. The inference tasks of determining consistency and deriving the minimal network are traditionally achieved by graph algorithms (e.g. Floyd-Warshall, Johnson) or by iteration of narrowing operators (e.g. ΔSTP). None of these methods exploits effectively the tree-decomposition structure of the constraint graph of an STN. Methods based on variable elimination (e.g. adaptive consistency) can exploit this structure, but have not been applied to STNs as far as they could, in part because it is unclear how to efficiently pass the ‘messages’ over continuous domains. We show that for an STN, these messages can be represented compactly as sub-STNs. We then present an efficient message-passing scheme for computing the minimal constraints of an STN. Analysis of this algorithm,Prop-STP, brings formal explanation of the performance of the existing STN solvers ΔSTP and SR-PC. Empirical results validate the efficiency of Prop-STP, demonstrating performance comparable to ΔSTP, in cases where the constraint graph is known to have small tree width, such as those that arise during Hierarchical Task Network planning.

An intensional approach to qualitative and quantitative periodicity-dependent temporal constraints

In this paper, we propose a framework for representing and reasoning about qualitative and quantitative temporal constraints between periodic events. In particular, our contribution is twofold: (i) we provide a formalism to deal with both qualitative and quantitative “periodicity-dependent” constraints between repeated events, considering user-defined periodicities as well; and (ii) we propose an intensional approach to temporal reasoning, which is based on the operations of intersection and composition. Such a comprehensive approach, to the best of our knowledge, represents an innovative contribution that integrates and extends results from both the artificial intelligence and the temporal databases literature. © 2009 Wiley Periodicals, Inc.

Causal temporal constraint networks for representing temporal knowledge

In this work we describe causal temporal constraint networks (CTCN) as a new computable model for representing temporal information and efficiently handling causality. The proposed model enables qualitative and quantitative temporal constraints to be established, introduces the representation of causal constraints, and suggests mechanisms for representing inexact temporal knowledge. The temporal handling of information is achieved by structuring the information in different interpretation contexts, linked to each other through an inference mechanism which obtains interpretations that are consistent with the original temporal information. In carrying out inferences, we take into account the temporal relationships between events, the possible inexactitude associated with the events, and the atemporal or static information which affects the interpretation pattern being considered. The proposed schema is illustrated with an application developed using the CommonKADS methodology.

On scene interpretation with description logics

We examine the possible use of description logics (DLs) as a knowledge representation and reasoning system for high-level scene interpretation. It is shown that so-called aggregates composed of multiple parts and constrained primarily by temporal and spatial relations can be used to represent high-level concepts such as object configurations, occurrences, events, and episodes that are required in an application context. Scene interpretation is modelled as a stepwise process which exploits the taxonomical and compositional relations between aggregate concepts while incorporating visual evidence and contextual information. It is shown that aggregates can be represented by concept expressions of a description logic which provides a concrete-domain extension for quantitative temporal and spatial constraints. The analysis reveals that different kinds of representation constructs have to be carefully selected in order to provide for the required expressivity while retaining decidability in general as well as practical support from description logic system implementations in particular. Reasoning services of the DL system can be used as building blocks for the interpretation process, but additional information is required to generate preferred interpretations. A probabilistic model is sketched which can be integrated with the knowledge-based framework.

Uncertainty in Soft Temporal Constraint Problems:A General Framework and Controllability Algorithms forThe Fuzzy Case

In real-life temporal scenarios, uncertainty and preferences are often essential and coexisting aspects. We present a formalism where quantitative temporal constraints with both preferences and uncertainty can be defined. We show how three classical notions of controllability (that is, strong, weak, and dynamic), which have been developed for uncertain temporal problems, can be generalized to handle preferences as well. After defining this general framework, we focus on problems where preferences follow the fuzzy approach, and with properties that assure tractability. For such problems, we propose algorithms to check the presence of the controllability properties. In particular, we show that in such a setting dealing simultaneously with preferences and uncertainty does not increase the complexity of controllability testing. We also develop a dynamic execution algorithm, of polynomial complexity, that produces temporal plans under uncertainty that are optimal with respect to fuzzy preferences.

Integrating quantitative and qualitative fuzzy temporal constraints

In this work we address the problem of representing and reasoning with temporal knowledge in a very general and flexible manner. To this aim we propose a model of integration of quantitative and qu...

Reasoning with Numeric and Symbolic Time Information

Representing and reasoning about time is fundamental in many applications of Artificial Intelligence as well as of other disciplines in computer science, such as scheduling, planning, computational linguistics, database design and molecular biology. The development of a domain-independent temporal reasoning system is then practically important. An important issue when designing such systems is the efficient handling of qualitative and metric time information. We have developed a temporal model, TemPro, based on the Allen interval algebra, to express and manage such information in terms of qualitative and quantitative temporal constraints. TemPro translates an application involving temporal information into a Constraint Satisfaction Problem (CSP). Constraint satisfaction techniques are then used to manage the different time information by solving the CSP. In order for the system to deal with real time applications or those applications where it is impossible or impractical to solve these problems completely, we have studied different methods capable of trading search time for solution quality when solving the temporal CSP. These methods are exact and approximation algorithms based respectively on constraint satisfaction techniques and local search. Experimental tests were performed on randomly generated temporal constraint problems as well as on scheduling problems in order to compare and evaluate the performance of the different methods we propose. The results demonstrate the efficiency of the MCRW approximation method to deal with under constrained and middle constrained problems while Tabu Search and SDRW are the methods of choice for over constrained problems.

CTP: A New Constraint-Based Formalism for Conditional, Temporal Planning

Temporal constraints pose a challenge for conditional planning, because it is necessary for a conditional planner to determine whether a candidate plan will satisfy the specified temporal constraints. This can be difficult, because temporal assignments that satisfy the constraints associated with one conditional branch may fail to satisfy the constraints along a different branch. In this paper we address this challenge by developing the Conditional Temporal Problem (CTP) formalism, an extension of standard temporal constraint-satisfaction processing models used in non-conditional temporal planning. Specifically, we augment temporal CSP frameworks by (1) adding observation nodes, and (2) attaching labels to all nodes to indicate the situation(s) in which each will be executed. Our extended framework allows for the construction of conditional plans that are guaranteed to satisfy complex temporal constraints. Importantly, this can be achieved even while allowing for decisions about the precise timing of actions to be postponed until execution time, thereby adding flexibility and making it possible to dynamically adapt the plan in response to the observations made during execution. We also show that, even for plans without explicit quantitative temporal constraints, our approach fixes a problem in the earlier approaches to conditional planning, which resulted in their being incomplete.

(U‐Th)/He chronometry experiences a renaissance

Radioactive decay of naturally occurring nuclides provides the basis for nearly all of the quantitative temporal constraints on geophysical phenomena, from climate dynamics on scales of 102–103 years, to planetary differentiation over 108–109 years. In general, there are few geologic problems that cannot be resolved by radioisotopic chronometers, partly because of the wide range of radioactive parent nuclides and decay rates, contrasting chemical behaviors of parent and daughter atoms during common geologic processes, and ever‐advancing geoanalytical techniques. Despite broad success, however, radioisotopic techniques are still difficult to apply to many important geologic phenomena requiring temporal constraints, such as the timing and rates of shallow crustal processes that are critical aspects of geomorphology and neotectonics.

Temporal Reasoning in Workflow Systems

In a workflow system, autonomous agents perform various activities cooperatively to complete a common task. Successful completion of the task often depends on correct synchronization and scheduling of agents' activities. It would greatly enhance the capabilities of current workflow systems if quantitative temporal constraints on the duration of activities and their synchronization requirements can be specified and reasoned about. This paper investigates such requirements and related reasoning algorithms. In particular, the paper studies the consistency, prediction and enactment services in a workflow system, and provides corresponding algorithms. The consistency service is to ensure that the specification of the temporal constraints is possible to satisfys the prediction service is to foretell the time frame for the involved activitiess and the enactment service is to schedule the activities so that, as long as each agent starts and finishes its task within the specified time period, the overall constraints will always be satisfied. For the enactment service, the paper identifies two practically interesting families of enactment schedules for autonomous agents, namely “free schedules” and “restricted due-time schedules”. In a free schedule, an agent may use any amount of time to finish the task as long as it is between the minimum and maximum time declared by the agent when the workflow is designed. A restricted due-time schedule is a more restrictive one in which the maximum amount of time that an agent may use is limited to a smaller number than the declared maximum. The paper presents efficient algorithms to find free and restricted due-time schedules. The paper also provides algorithms for the above services when multiple time granularities are involved in the temporal constraint specification.